Discrete Optimization has experienced a tremendous growth in the last decades, both in its theoretical and practical sides, partially provoked by the emergence of new computational resources as well as real-world applications that have boosted this growth. This impulse has also motivated the use of Discrete Optimization models to deal with problems involving a large number of variables and constraints, that years before would have not been possible to be dealt with. One of the fields in which Discrete Optimization has caused a larger impact is in Machine Learning. The incorporation of binary decisions to the classical approaches as Support Vector Machine [1]}, Classification Trees [2]}, Linear Regression and Clustering, amongst other, has considerably enhanced their performance in terms of accuracy and interpretability (see e.g. [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}). In particular, one of the most interesting applications of Machine Learning is that related with Supervised Classification.
